Learning group-wise spatial attention and label dependencies for multi-task thoracic disease classification.

This paper considers the multi-label thoracic abnormality classification with chest X-ray images. In clinical settings, Chest X-ray imaging is a general diagnostic tool applied to visualize numerous thoracic pathological changes. While deep learning techniques have been extensively tested in this field, certain challenges persist. The data in existing thoracic abnormality datasets is insufficient, and some diseases are extremely imbalanced. Meanwhile, the dependencies between different labels are often ignored. To tackle these issues head-on, this paper introduces two crucial modules: the group-wise spatial attention (GWSA) module and the label co-occurrence dependency (LCD) module, integrated with DenseNet121 backbone. Specifically, GWSA enhances the spatial features within distinct groups while keeping the between-group feature discrimination. LCD models the correlations between different thoracic abnormalities to refine the predicted probabilities. In conjunction with the DenseNet121 backbone, these two modules reach an average AUC score of 0.818 on Chest X-ray14 dataset, achieving state-of-the-art. Source code is available at https://github.com/YujiaKCL/Chest-Xray14-GWSA-LCD. [ABSTRACT FROM AUTHOR]